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Reinforcement Learning Jobs (NOW HIRING)

ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. This is a hands-on ...

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation: Competitive Our client is an elite development firm and a high-growth software company responsible for ...

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Reinforcement Learning information

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$28.5K

$58.3K

$80K

How much do reinforcement learning jobs pay per year?

As of Jul 7, 2026, the average yearly pay for reinforcement learning in the United States is $58,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,500.00 and $68,000.00 per year, depending on experience, location, and employer.

Will MLE be replaced by AI?

In reinforcement learning, machine learning engineers (MLEs) design, implement, and optimize algorithms that enable AI systems to learn from interactions. While AI continues to advance, MLEs play a crucial role in developing and fine-tuning models, and their skills remain essential for deploying effective reinforcement learning solutions. The role is evolving with increased automation, but MLEs are unlikely to be fully replaced in the near term.

What are the common responsibilities of a Reinforcement Learning professional on a daily basis?

A typical day for a Reinforcement Learning professional involves designing and implementing learning algorithms, running experiments, analyzing data, and iterating on models to improve performance. You might collaborate closely with data scientists, software engineers, and product managers to integrate your solutions into broader systems or products. Regular activities also include reading recent research literature and participating in team meetings to discuss progress and obstacles. This dynamic role often balances deep technical work with teamwork to drive innovative applications in areas such as robotics, recommendation systems, or autonomous systems.

What are the key skills and qualifications needed to thrive in the Reinforcement Learning position, and why are they important?

To thrive in a Reinforcement Learning role, you need a solid background in mathematics, statistics, machine learning, and programming (commonly with Python), typically supported by a relevant degree such as in computer science or engineering. Experience with frameworks like TensorFlow, PyTorch, OpenAI Gym, and familiarity with large-scale computing systems are highly valued. Strong problem-solving abilities, curiosity, and effective collaboration and communication skills help you excel in multidisciplinary research and project teams. These capabilities are crucial for designing, implementing, and refining complex algorithms that learn from interaction to solve real-world problems.

What engineers make $500,000?

Senior reinforcement learning engineers with extensive experience, advanced skills in machine learning frameworks, and a strong track record in deploying AI systems can earn salaries around $500,000 or higher, especially in top tech companies or specialized research roles. Compensation often includes base salary, bonuses, and stock options, reflecting expertise in AI, deep learning, and programming languages like Python or C++.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior reinforcement learning engineer or research scientist, often requiring advanced skills in machine learning, deep learning, and programming. These roles usually involve leading projects, developing innovative algorithms, and may require extensive experience and specialized certifications. Compensation at this level reflects the expertise and impact expected in cutting-edge AI development environments.

What is a Reinforcement Learning job?

A Reinforcement Learning (RL) job involves designing, developing, and optimizing algorithms that enable machines to learn from interactions with their environment. RL professionals work on applications in robotics, finance, gaming, and autonomous systems, leveraging techniques like deep reinforcement learning and policy optimization. Responsibilities often include researching new models, implementing RL algorithms, and improving AI performance. Strong programming skills, knowledge of machine learning frameworks, and an understanding of mathematical concepts like probability and optimization are essential.

Which 3 jobs will survive AI?

Reinforcement Learning specialists, data scientists, and AI ethics professionals are likely to remain in demand as AI advances, due to their specialized skills in developing, managing, and overseeing AI systems. These roles require advanced knowledge of algorithms, programming, and ethical considerations, making them less susceptible to automation. Continuous learning and expertise in AI tools and frameworks are essential for long-term job security in these fields.
More about Reinforcement Learning jobs
What cities are hiring for Reinforcement Learning jobs? Cities with the most Reinforcement Learning job openings:
What are the most commonly searched types of Reinforcement Learning jobs? The most popular types of Reinforcement Learning jobs are:
What states have the most Reinforcement Learning jobs? States with the most job openings for Reinforcement Learning jobs include:
Infographic showing various Reinforcement Learning job openings in the United States as of July 2026, with employment types broken down into 3% Internship, 56% Full Time, 35% Part Time, and 6% Contract. Highlights an 100% In-person job distribution, with an average salary of $58,347 per year, or $28.1 per hour.
Data Scientist, Reinforcement Learning

Data Scientist, Reinforcement Learning

ExxonMobil

Spring, TX

Other

Medical, Dental, Vision, Life, Retirement

Re-posted 7 days ago


ExxonMobil rating

6.0

Company rating: 6.0 out of 10

Based on 225 frontline employees who took The Breakroom Quiz

53rd of 74 rated oil and gas companies


Job description

Your role on our team

Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across ExxonMobil's upstream, downstream, and commercial operations.


Collaborate with engineers, scientists, and business stakeholders to turn complex operational and planning problems into deployable, production-grade RL solutions.


Advance the organization's capabilities in reinforcement learning, decision optimization, and autonomous control as part of the Modeling, Optimization, and Data Science (MODS) team.

What you will do
  • Design, develop, and deploy reinforcement learning solutions for real-world energy applications such as production optimization, process control, supply chain scheduling, drilling optimization, and resource allocation.
  • Formulate sequential decision problems by defining state spaces, action spaces, reward structures, transition dynamics, and operational constraints with domain experts.
  • Develop RL agents using model-free methods (e.g., PPO, SAC, TD3, DQN where appropriate) and model-based approaches, selecting methods based on problem requirements, safety, and data availability.
  • Build and use simulation environments and digital twins for offline training, policy evaluation, and validation before real-world deployment.
  • Apply safe and constrained RL techniques to ensure agents operate within operational and safety limits.
  • Integrate RL solutions with existing optimization, simulation, and control systems across real-time and planning use cases.
  • Partner with data scientists and ML engineers to operationalize solutions, including training pipelines, monitoring, retraining, and performance tracking.
  • Benchmark RL against traditional methods such as LP, MIP, heuristic search, MPC, and stochastic optimization to identify best-fit approaches.
  • Stay current with advances in offline RL, safe RL, multi-agent RL, hierarchical RL, and model-based RL.
  • Share knowledge, publish findings where appropriate, and mentor peers on RL best practices.
About you

Desired Skills:

  • Experienced AI/ML professional with strong expertise in reinforcement learning, sequential decision-making, optimization, and real-world deployment.
  • 5+ years of experience in AI/ML, optimization, or related fields, including at least 2 years in reinforcement learning, sequential decision-making, or optimal control.
  • Master's or PhD in Computer Science, Machine Learning, Operations Research, Control Theory, Robotics, Applied Mathematics, Engineering, or a related quantitative field.
  • Deep understanding of RL fundamentals, including MDPs, dynamic programming, temporal-difference learning, policy gradients, and actor-critic methods.
  • Proven experience building RL systems end-to-end, from environment and reward design through training, evaluation, and deployment.
  • Experience with simulation environments, digital twins, or system models.
  • Strong background in statistics, probability, optimization, control theory, and algorithm design.
  • Proficiency in Python, PyTorch and/or TensorFlow, plus RL tools such as Stable Baselines3, RLlib, and Gymnasium.
  • Strong communication and collaboration skills, including the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills:

  • Experience applying RL or decision optimization in industrial domains such as process control, robotics, autonomous systems, supply chain, energy systems, or operations research.
  • Familiarity with offline (batch) RL, safe RL, and multi-agent RL.
  • Knowledge of model-based RL, MPC, and hybrid RL-control approaches.
  • Understanding of classical optimization methods and how RL complements them.
  • Experience with physics-informed or hybrid mechanistic/ML modeling and domain-informed reward or constraint design.
  • Familiarity with platforms such as Azure ML, Azure OpenAI, Databricks, and MLOps tools such as MLflow or Weights & Biases.
  • Experience in the energy industry or other asset-intensive, safety-critical sectors.
Your benefits

An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
 

We offer you: 
 

  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life. 
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match. 
  • Workplace Flexibility: We have several programs such as "Flex your Day", providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans. 
  • Culture of Health: Programs and resources to support your wellbeing. 
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you. 
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.
     

More information on our Company's benefits can be found at  www.exxonmobilfamily.com.
 

Please note benefits may be changed from time to time without notice, subject to applicable law.

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